rANOMALY use case
Etienne Rifa & Sebastien Theil
17/08/2020
1 rANOMALY step-by-step use case.
1.1 Help
Each function have a detailed help accessible in R via ?{funtion}.
1.2 Tests datasets
The dataset can be downloaded via this link.
This tutorial assume that you have extracted all the read file in a folder named reads along with the sample-metadata.csv file.
We share a 24 samples test dataset extract from rats feces at two different time (t0 & t50) and in two nutrition conditions. Also included two extraction control sample (blank).
sm <- read.table("sample_metadata.csv", sep="\t",header=TRUE)
DT::datatable(sm)load("decontam_out/robjects.Rdata")1.3 ASV definition with DADA2
The first step will be the creation of ASVs (Amplicon Sequence Variants) thanks to the dada2 package. In rANOMALY, only one function is needed to compute all the different steps require from this package.
Sample names will be extracted from the file name, so files must be formatted as followed : {sample-id1}_R1.fastq.gz {sample-id1}_R2.fastq.gz etc…
dada_res = dada2_fun(path="./reads", dadapool = "pseudo", compress=TRUE, plot=FALSE)Main output: - read_tracking.csv that summarize the read number after each filtering step.
DT::datatable(read.table("dada2_out/read_tracking.csv",sep="\t",header=TRUE))The sample names extracted from the file name. We consider as sample name anything that is before the first underscore. This must match the sample names that are in sample metadata files. input: raw read number. filtered: after dada2 filtering step: no N’s in sequence, low quality, and phiX. denoisedF & denoisedR: after denoising. Forward & Reverse. merged: after merging R1 & R2. nonchim: after chimeras filtering.
dada2_robjects.Rdatawith raw ASV table and representative sequences in objectsotu.table,seqtab.export&seqtab.nochim.raw_asv-table.csvrep-seqs.fna
1.4 Taxonomic assignment
This function uses IDTAXA function from DECIPHER package, and allows to use 2 differents databases. It keeps the best assignation on 2 criteria, resolution (depth) and confidence. The final taxonomy is validated by multiple ancestors taxa and incongruity correction step.
We share the latest databases we use in the IDTAXA format in this link. You can also generate your own database following those instructions and scripts we provide in another repository.
tax.table = assign_taxo_fun(dada_res = dada_res, id_db = c("path_to_your_banks/silva/SILVA_SSU_r132_March2018.RData","path_to_your_banks/DAIRYdb_v1.2.0_20190222_IDTAXA.RData") )Main output: - taxo_robjects.Rdata with taxonomy in phyloseq format in tax.table object. - final_tax_table.csv the final assignation table that will be use in next steps. - allDB_tax_table.csv raw assignations from the two databases, mainly for debugging.
1.5 Phylogenetic Tree
The phylogenetic tree from the representative sequences is generated using phangorn and DECIPHER packages.
tree = generate_tree_fun(dada_res)Main output: - tree_robjects.Rdata with phylogenetic tree object in phyloseq format.
1.6 Phyloseq object
To create a phyloseq object, we need to merge four objects and one file: - the asv table otu.table and the representative sequences seqtab.nochim from dada2_robjects.Rdata - a taxonomy table taxo_robjects.Rdata from taxo_robjects.Rdata - the phylogenetic tree tree from tree_robjects.Rdata - metadata from sample-metadata.csv
data = generate_phyloseq_fun(dada_res = dada_res, taxtable = tax.table, tree = tree, metadata = "./sample_metadata.csv")Main output: - robjects.Rdata with phyloseq object in data for raw counts and data_rel for relative abundance.
1.7 Decontamination
The decontam_fun function uses decontam R package with control samples to filter contaminants. The decontam package offers two main methods, frequency and prevalence (and then you can combine those methods). For frequency method, it is mandatory to have the dna concentration of each sample in phyloseq (and hence in the sample-metadata.csv). “In this method, the distribution of the frequency of each sequence feature as a function of the input DNA concentration is used to identify contaminants.” In the prevalence methods no need of DNA quantification. “In this method, the prevalence (presence/absence across samples) of each sequence feature in true positive samples is compared to the prevalence in negative controls to identify contaminants.”
Tips: sequencing plateforms often quantify the DNA before sequencing, but do not automaticaly give the information. Just ask for it ;).
Our function integrates the basics ASV frequency (nb_reads_ASV/nb_total_reads) and prevalence (nb_sample_ASV/nb_total_sample) filtering. As in our lab we had a known recurrent contaminant we included an option to filter out ASV based on they taxa names.
data = decontam_fun(data = data, domain = "Bacteria", column = "type", ctrl_identifier = "control", spl_identifier = "sample", number = 100)Main output: - robjects.Rdata with contaminant filtered phyloseq object named data. - Exclu_out.csv list of filtered ASVs for each filtering step. - Kronas before and after filtering. - raw_asv-table.csv & relative_asv-table.csv. - venndiag_filtering.png.
venndiag
1.8 Plots, diversity and statistics
!!! We are currently developping a ShinyApp to visualize your data, sub-select your samples/taxons and do all those analyses interactively !!! ExploreMetabar
1.8.1 Rarefaction curves
In order to observe the sampling depth of each samples we start by plotting rarefactions curves. Those plots are generated by Plotly which makes the plots interactive.
rarefaction(data, "souche_temps", 100 )## rarefying sample SB1-Sauv0
## rarefying sample SB10-Mut0
## rarefying sample SB11-Mut0
## rarefying sample SB12-Mut0
## rarefying sample SB13-Sauv50
## rarefying sample SB14-Sauv50
## rarefying sample SB15-Sauv50
## rarefying sample SB16-Sauv50
## rarefying sample SB17-Sauv50
## rarefying sample SB18-Sauv50
## rarefying sample SB19-Mut50
## rarefying sample SB2-Sauv0
## rarefying sample SB20-Mut50
## rarefying sample SB21-Mut50
## rarefying sample SB22-Mut50
## rarefying sample SB23-Mut50
## rarefying sample SB24-Mut50
## rarefying sample SB3-Sauv0
## rarefying sample SB4-Sauv0
## rarefying sample SB5-Sauv0
## rarefying sample SB6-Sauv0
## rarefying sample SB7-Mut0
## rarefying sample SB8-Mut0
## rarefying sample SB9-Mut0## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.1.8.2 Composition plots
Composition plots reveals here the top 10 genus present in our samples. #TODO Ord1 option control the… Fact1 option control the…
1.8.2.1 Relative abundance
bars_fun(data = data, top = 10, Ord1 = "souche_temps", Fact1 = "souche_temps", rank="Genus", relative = TRUE)1.8.2.2 Raw abundance
bars_fun(data = data, top = 10, Ord1 = "souche_temps", Fact1 = "souche_temps", rank="Genus", relative = FALSE)1.8.3 Diversity analyses
1.8.3.1 Alpha diversity
This function computes various alpha diversity indexes and returns: - a boxplot comparing conditions. - a table of values - an ANOVA analysis - a wilcox result test comparing conditions and giving the significativity of the observed differences. - a mixture model if your data include repetition in sampling. All this in a single function.
alpha <- diversity_alpha_fun(data = data, output = "./plot_div_alpha/", column1 = "souche", column2 = "temps",
column3 = "", supcovs = "", measures = c("Observed", "Shannon") )## [1] "coucou"
## INFO [2020-08-20 16:22:24] Alpha diversity tab ...
## INFO [2020-08-20 16:22:24] Done.
## INFO [2020-08-20 16:22:24] Plotting ...
## INFO [2020-08-20 16:22:24] Done.## INFO [2020-08-20 16:22:25] ANOVA ...
## INFO [2020-08-20 16:22:25] Done.
## INFO [2020-08-20 16:22:25] Finish.1.8.3.1.1 Table of values
The table of values for each indices you choose to compute.
pander(alpha$alphatable, style='rmarkdown')| Observed | Shannon | |
|---|---|---|
| SB1.Sauv0 | 41 | 1.477 |
| SB10.Mut0 | 40 | 2.073 |
| SB11.Mut0 | 51 | 2.178 |
| SB12.Mut0 | 38 | 2.116 |
| SB13.Sauv50 | 46 | 2.691 |
| SB14.Sauv50 | 57 | 2.905 |
| SB15.Sauv50 | 50 | 2.793 |
| SB16.Sauv50 | 52 | 2.8 |
| SB17.Sauv50 | 49 | 2.624 |
| SB18.Sauv50 | 54 | 2.831 |
| SB19.Mut50 | 66 | 2.638 |
| SB2.Sauv0 | 26 | 2.099 |
| SB20.Mut50 | 72 | 2.721 |
| SB21.Mut50 | 79 | 3.062 |
| SB22.Mut50 | 81 | 2.81 |
| SB23.Mut50 | 84 | 3.175 |
| SB24.Mut50 | 90 | 3.148 |
| SB3.Sauv0 | 19 | 0.1962 |
| SB4.Sauv0 | 41 | 2.52 |
| SB5.Sauv0 | 46 | 1.923 |
| SB6.Sauv0 | 46 | 1.067 |
| SB7.Mut0 | 33 | 2.256 |
| SB8.Mut0 | 58 | 2.089 |
| SB9.Mut0 | 50 | 2.237 |
1.8.3.2 Boxplots
The boxplots of those values.
alpha$plot
1.8.3.3 ANOVA results
For each indices, you have access to the ANOVA test. Here we present the result for the “Observed” indice.
pander(alpha$Observed$anova)| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Depth | 1 | 49.36 | 49.36 | 0.5091 | 0.4838 |
| souche | 1 | 1877 | 1877 | 19.36 | 0.0002764 |
| temps | 1 | 3649 | 3649 | 37.64 | 5.392e-06 |
| Residuals | 20 | 1939 | 96.96 | NA | NA |
1.8.3.4 Wilcox test
Wilcox tests are made on each factor you have entered, and the combination of the two. Here “souche” and “temps”.
1.8.3.4.1 Wilcox test for “souche” factor
pander(alpha$Observed$wilcox_col1)| mutant | |
|---|---|
| sauvage | 0.043 |
1.8.3.4.2 Wilcox test for “temps” factor FDR corrected.
pander(alpha$Observed$wilcox_col2_fdr)| t0 | |
|---|---|
| t50 | 0.001 |
1.8.3.4.3 Wilcox test for the collapsed factors
pander(alpha$Observed$wilcox_col2_collapsed)| mutant_t0 | mutant_t50 | sauvage_t0 | |
|---|---|---|---|
| mutant_t50 | 0.002 | NA | NA |
| sauvage_t0 | 0.377 | 0.005 | NA |
| sauvage_t50 | 0.336 | 0.002 | 0.008 |
1.8.3.5 Beta diversity
beta <- diversity_beta_fun(data = data, output = "./plot_div_beta/", glom = "ASV", column1 = "temps", column2 = "souche", covar ="")## INFO [2020-08-20 16:22:26] Option1...
## [1] "t0" "t50"
## INFO [2020-08-20 16:22:26] Split table t0...
## INFO [2020-08-20 16:22:26] Done.
## [1] ""
## INFO [2020-08-20 16:22:26] No glom ...
## INFO [2020-08-20 16:22:26] Bray ...
##
## mutant sauvage
## 6 6
## INFO [2020-08-20 16:22:27] Done
## INFO [2020-08-20 16:22:27] Unifrac ...
## INFO [2020-08-20 16:22:27] Done
## INFO [2020-08-20 16:22:27] wunifrac ...
## INFO [2020-08-20 16:22:27] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.53973 0.53973 2.8355 0.17954 0.02398 *
## souche 1 0.75338 0.75338 3.9580 0.25061 0.01199 *
## Residuals 9 1.71311 0.19035 0.56985
## Total 11 3.00623 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.952842 4.640344 0.3169559 0.008 0.008 *
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.12045 0.120447 1.6362 0.12272 0.14486
## souche 1 0.19850 0.198504 2.6965 0.20225 0.01299 *
## Residuals 9 0.66253 0.073615 0.67503
## Total 11 0.98148 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.2429196 3.289082 0.2475026 0.003 0.003 *
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.51694 0.51694 5.3962 0.32059 0.002997 **
## souche 1 0.23337 0.23337 2.4360 0.14472 0.059940 .
## Residuals 9 0.86218 0.09580 0.53469
## Total 11 1.61249 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.3815338 3.099498 0.236612 0.045 0.045 .
## INFO [2020-08-20 16:22:27] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1383252
## Run 1 stress 0.1415928
## Run 2 stress 0.138325
## ... New best solution
## ... Procrustes: rmse 0.0008296711 max resid 0.001748451
## ... Similar to previous best
## Run 3 stress 0.2163921
## Run 4 stress 0.1383259
## ... Procrustes: rmse 0.0002572591 max resid 0.0005194362
## ... Similar to previous best
## Run 5 stress 0.1471255
## Run 6 stress 0.1383278
## ... Procrustes: rmse 0.00116331 max resid 0.002057887
## ... Similar to previous best
## Run 7 stress 0.1383248
## ... New best solution
## ... Procrustes: rmse 0.0002843611 max resid 0.0006506299
## ... Similar to previous best
## Run 8 stress 0.1383249
## ... Procrustes: rmse 0.0001756664 max resid 0.0004123756
## ... Similar to previous best
## Run 9 stress 0.1416605
## Run 10 stress 0.2136644
## Run 11 stress 0.1383256
## ... Procrustes: rmse 0.0005725732 max resid 0.00107865
## ... Similar to previous best
## Run 12 stress 0.1416638
## Run 13 stress 0.1415945
## Run 14 stress 0.2172086
## Run 15 stress 0.1383266
## ... Procrustes: rmse 0.001067344 max resid 0.001929848
## ... Similar to previous best
## Run 16 stress 0.2451062
## Run 17 stress 0.1383281
## ... Procrustes: rmse 0.001509928 max resid 0.002812518
## ... Similar to previous best
## Run 18 stress 0.2288809
## Run 19 stress 0.1416399
## Run 20 stress 0.1383251
## ... Procrustes: rmse 0.000555828 max resid 0.001111333
## ... Similar to previous best
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1383248
## Run 1 stress 0.2307729
## Run 2 stress 0.1383264
## ... Procrustes: rmse 0.001278423 max resid 0.002377285
## ... Similar to previous best
## Run 3 stress 0.1383276
## ... Procrustes: rmse 0.001697241 max resid 0.003017132
## ... Similar to previous best
## Run 4 stress 0.1383247
## ... New best solution
## ... Procrustes: rmse 0.0001894508 max resid 0.0004286926
## ... Similar to previous best
## Run 5 stress 0.141657
## Run 6 stress 0.2172085
## Run 7 stress 0.1383248
## ... Procrustes: rmse 0.0001797545 max resid 0.0003307533
## ... Similar to previous best
## Run 8 stress 0.2147061
## Run 9 stress 0.1415924
## Run 10 stress 0.1416469
## Run 11 stress 0.1383253
## ... Procrustes: rmse 0.0005195144 max resid 0.001271579
## ... Similar to previous best
## Run 12 stress 0.2116428
## Run 13 stress 0.1416665
## Run 14 stress 0.1383248
## ... Procrustes: rmse 9.677855e-05 max resid 0.0001383072
## ... Similar to previous best
## Run 15 stress 0.1415946
## Run 16 stress 0.1383253
## ... Procrustes: rmse 0.0004864877 max resid 0.00118181
## ... Similar to previous best
## Run 17 stress 0.138325
## ... Procrustes: rmse 0.0004131526 max resid 0.0009212723
## ... Similar to previous best
## Run 18 stress 0.1383294
## ... Procrustes: rmse 0.001951796 max resid 0.003530348
## ... Similar to previous best
## Run 19 stress 0.223134
## Run 20 stress 0.2136644
## *** Solution reached
## Run 0 stress 0.1396049
## Run 1 stress 0.2004114
## Run 2 stress 0.2004113
## Run 3 stress 0.1554504
## Run 4 stress 0.1396049
## ... Procrustes: rmse 5.734123e-05 max resid 0.0001207403
## ... Similar to previous best
## Run 5 stress 0.139605
## ... Procrustes: rmse 0.0001539508 max resid 0.0003112719
## ... Similar to previous best
## Run 6 stress 0.2847186
## Run 7 stress 0.1396049
## ... New best solution
## ... Procrustes: rmse 2.046337e-05 max resid 4.386856e-05
## ... Similar to previous best
## Run 8 stress 0.1396049
## ... Procrustes: rmse 9.114325e-06 max resid 1.837286e-05
## ... Similar to previous best
## Run 9 stress 0.1396049
## ... Procrustes: rmse 2.266558e-05 max resid 5.025931e-05
## ... Similar to previous best
## Run 10 stress 0.1396049
## ... Procrustes: rmse 4.119668e-06 max resid 8.658105e-06
## ... Similar to previous best
## Run 11 stress 0.1396049
## ... Procrustes: rmse 0.000104347 max resid 0.0002132518
## ... Similar to previous best
## Run 12 stress 0.1554505
## Run 13 stress 0.1554513
## Run 14 stress 0.1941377
## Run 15 stress 0.139605
## ... Procrustes: rmse 0.0001882369 max resid 0.000378769
## ... Similar to previous best
## Run 16 stress 0.1396049
## ... Procrustes: rmse 0.0001488906 max resid 0.0003177032
## ... Similar to previous best
## Run 17 stress 0.1396049
## ... Procrustes: rmse 8.27646e-05 max resid 0.0001748531
## ... Similar to previous best
## Run 18 stress 0.1396049
## ... Procrustes: rmse 0.000143443 max resid 0.0003061739
## ... Similar to previous best
## Run 19 stress 0.2340567
## Run 20 stress 0.2603873
## *** Solution reached
## Run 0 stress 0.04595665
## Run 1 stress 0.08158088
## Run 2 stress 0.08294231
## Run 3 stress 0.3473089
## Run 4 stress 0.04838974
## Run 5 stress 0.05318677
## Run 6 stress 0.04595612
## ... New best solution
## ... Procrustes: rmse 0.001060725 max resid 0.002600651
## ... Similar to previous best
## Run 7 stress 0.05318649
## Run 8 stress 0.05106695
## Run 9 stress 0.08373163
## Run 10 stress 0.04595697
## ... Procrustes: rmse 0.0002297223 max resid 0.0005445442
## ... Similar to previous best
## Run 11 stress 0.08294096
## Run 12 stress 0.08157914
## Run 13 stress 0.04838885
## Run 14 stress 0.08157822
## Run 15 stress 0.08256976
## Run 16 stress 0.05106339
## Run 17 stress 0.05106757
## Run 18 stress 0.04595626
## ... Procrustes: rmse 4.395981e-05 max resid 0.0001025333
## ... Similar to previous best
## Run 19 stress 0.04595633
## ... Procrustes: rmse 6.689515e-05 max resid 0.0001611577
## ... Similar to previous best
## Run 20 stress 0.05319174
## *** Solution reached
## INFO [2020-08-20 16:22:28] Done.
## INFO [2020-08-20 16:22:28] Saving ...## INFO [2020-08-20 16:22:30] Supplement Beta plots ...
## INFO [2020-08-20 16:22:30] Done.
## INFO [2020-08-20 16:22:30] Split table t50...
## INFO [2020-08-20 16:22:30] Done.
## [1] ""
## INFO [2020-08-20 16:22:30] No glom ...
## INFO [2020-08-20 16:22:30] Bray ...
##
## mutant sauvage
## 6 6
## INFO [2020-08-20 16:22:30] Done
## INFO [2020-08-20 16:22:30] Unifrac ...
## INFO [2020-08-20 16:22:30] Done
## INFO [2020-08-20 16:22:30] wunifrac ...
## INFO [2020-08-20 16:22:31] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.06369 0.06369 3.118 0.03093 0.091908 .
## souche 1 1.81185 1.81185 88.707 0.87981 0.000999 ***
## Residuals 9 0.18383 0.02043 0.08926
## Total 11 2.05937 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 1.817719 75.21929 0.8826557 0.003 0.003 *
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.08686 0.08686 6.094 0.08776 0.010989 *
## souche 1 0.77457 0.77457 54.339 0.78261 0.000999 ***
## Residuals 9 0.12829 0.01425 0.12962
## Total 11 0.98972 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.7765885 36.43771 0.7846578 0.003 0.003 *
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.00813 0.00813 2.452 0.02254 0.141858
## souche 1 0.32283 0.32283 97.317 0.89471 0.001998 **
## Residuals 9 0.02986 0.00332 0.08274
## Total 11 0.36082 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.3244241 89.13773 0.8991302 0.005 0.005 *
## INFO [2020-08-20 16:22:31] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 7.297422e-05
## Run 1 stress 8.45789e-05
## ... Procrustes: rmse 0.0001215438 max resid 0.0002227982
## ... Similar to previous best
## Run 2 stress 9.207008e-05
## ... Procrustes: rmse 6.328125e-05 max resid 0.0001237073
## ... Similar to previous best
## Run 3 stress 9.829877e-05
## ... Procrustes: rmse 0.0002645478 max resid 0.000608853
## ... Similar to previous best
## Run 4 stress 9.716562e-05
## ... Procrustes: rmse 0.0002436066 max resid 0.0006010068
## ... Similar to previous best
## Run 5 stress 0.3058599
## Run 6 stress 9.46279e-05
## ... Procrustes: rmse 0.0002499175 max resid 0.0005774111
## ... Similar to previous best
## Run 7 stress 8.757103e-05
## ... Procrustes: rmse 6.716841e-05 max resid 0.0001251571
## ... Similar to previous best
## Run 8 stress 9.195979e-05
## ... Procrustes: rmse 8.073643e-05 max resid 0.0001739123
## ... Similar to previous best
## Run 9 stress 0.3140419
## Run 10 stress 9.586115e-05
## ... Procrustes: rmse 0.0002378274 max resid 0.0005885461
## ... Similar to previous best
## Run 11 stress 9.569161e-05
## ... Procrustes: rmse 0.0002584231 max resid 0.0005962577
## ... Similar to previous best
## Run 12 stress 9.926251e-05
## ... Procrustes: rmse 0.0002480177 max resid 0.0006144776
## ... Similar to previous best
## Run 13 stress 9.712931e-05
## ... Procrustes: rmse 0.00022982 max resid 0.0005743353
## ... Similar to previous best
## Run 14 stress 9.466725e-05
## ... Procrustes: rmse 0.0001141341 max resid 0.0002334227
## ... Similar to previous best
## Run 15 stress 9.52215e-05
## ... Procrustes: rmse 0.0002329505 max resid 0.000578527
## ... Similar to previous best
## Run 16 stress 9.940682e-05
## ... Procrustes: rmse 7.611758e-05 max resid 0.000133508
## ... Similar to previous best
## Run 17 stress 9.552615e-05
## ... Procrustes: rmse 0.0002390993 max resid 0.0005949772
## ... Similar to previous best
## Run 18 stress 8.147638e-05
## ... Procrustes: rmse 6.517806e-05 max resid 0.0001300267
## ... Similar to previous best
## Run 19 stress 9.97229e-05
## ... Procrustes: rmse 0.0002669821 max resid 0.0006146439
## ... Similar to previous best
## Run 20 stress 9.798424e-05
## ... Procrustes: rmse 0.000263625 max resid 0.000607186
## ... Similar to previous best
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 8.694326e-05
## Run 1 stress 9.854982e-05
## ... Procrustes: rmse 8.080336e-05 max resid 0.0001753304
## ... Similar to previous best
## Run 2 stress 9.382942e-05
## ... Procrustes: rmse 0.0001457875 max resid 0.0002709259
## ... Similar to previous best
## Run 3 stress 8.898157e-05
## ... Procrustes: rmse 0.000245596 max resid 0.0006366559
## ... Similar to previous best
## Run 4 stress 9.411321e-05
## ... Procrustes: rmse 0.00024977 max resid 0.0006441361
## ... Similar to previous best
## Run 5 stress 9.942027e-05
## ... Procrustes: rmse 0.0002530989 max resid 0.0006966849
## ... Similar to previous best
## Run 6 stress 9.297371e-05
## ... Procrustes: rmse 2.102032e-05 max resid 4.617638e-05
## ... Similar to previous best
## Run 7 stress 9.894972e-05
## ... Procrustes: rmse 0.000109759 max resid 0.0001972609
## ... Similar to previous best
## Run 8 stress 9.007518e-05
## ... Procrustes: rmse 6.146687e-05 max resid 0.0001615927
## ... Similar to previous best
## Run 9 stress 9.900251e-05
## ... Procrustes: rmse 0.0001511565 max resid 0.0003268108
## ... Similar to previous best
## Run 10 stress 9.277227e-05
## ... Procrustes: rmse 5.949235e-05 max resid 0.0001621835
## ... Similar to previous best
## Run 11 stress 9.406521e-05
## ... Procrustes: rmse 0.0002407634 max resid 0.0006704454
## ... Similar to previous best
## Run 12 stress 9.894569e-05
## ... Procrustes: rmse 8.11661e-05 max resid 0.000167026
## ... Similar to previous best
## Run 13 stress 8.809663e-05
## ... Procrustes: rmse 0.0002260571 max resid 0.0006346435
## ... Similar to previous best
## Run 14 stress 9.781974e-05
## ... Procrustes: rmse 0.0001281381 max resid 0.0002442458
## ... Similar to previous best
## Run 15 stress 8.987769e-05
## ... Procrustes: rmse 6.61466e-05 max resid 0.0001746144
## ... Similar to previous best
## Run 16 stress 9.646128e-05
## ... Procrustes: rmse 2.260404e-05 max resid 4.839915e-05
## ... Similar to previous best
## Run 17 stress 9.249083e-05
## ... Procrustes: rmse 0.0002362189 max resid 0.0006590408
## ... Similar to previous best
## Run 18 stress 9.135082e-05
## ... Procrustes: rmse 6.491662e-05 max resid 0.00018272
## ... Similar to previous best
## Run 19 stress 9.665503e-05
## ... Procrustes: rmse 0.0002372702 max resid 0.0006617
## ... Similar to previous best
## Run 20 stress 9.649675e-05
## ... Procrustes: rmse 0.0002438222 max resid 0.0006780024
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 9.276606e-05
## Run 1 stress 9.963832e-05
## ... Procrustes: rmse 6.473669e-05 max resid 0.0001693699
## ... Similar to previous best
## Run 2 stress 9.903512e-05
## ... Procrustes: rmse 0.0001688132 max resid 0.0003505997
## ... Similar to previous best
## Run 3 stress 8.961392e-05
## ... New best solution
## ... Procrustes: rmse 0.0001910005 max resid 0.0004478852
## ... Similar to previous best
## Run 4 stress 9.865541e-05
## ... Procrustes: rmse 0.0001994816 max resid 0.0004524879
## ... Similar to previous best
## Run 5 stress 9.254986e-05
## ... Procrustes: rmse 0.0001949915 max resid 0.0004590641
## ... Similar to previous best
## Run 6 stress 9.060173e-05
## ... Procrustes: rmse 0.0001908197 max resid 0.0004472175
## ... Similar to previous best
## Run 7 stress 9.541876e-05
## ... Procrustes: rmse 0.0002075558 max resid 0.0004750331
## ... Similar to previous best
## Run 8 stress 9.966815e-05
## ... Procrustes: rmse 0.0001633148 max resid 0.0004276814
## ... Similar to previous best
## Run 9 stress 9.458347e-05
## ... Procrustes: rmse 0.0001861985 max resid 0.0004133003
## ... Similar to previous best
## Run 10 stress 9.947262e-05
## ... Procrustes: rmse 0.0001894483 max resid 0.0004865836
## ... Similar to previous best
## Run 11 stress 9.153207e-05
## ... Procrustes: rmse 0.0001122695 max resid 0.0002880034
## ... Similar to previous best
## Run 12 stress 9.714027e-05
## ... Procrustes: rmse 0.0001138687 max resid 0.0002591533
## ... Similar to previous best
## Run 13 stress 0.2395887
## Run 14 stress 0.3358428
## Run 15 stress 9.749958e-05
## ... Procrustes: rmse 0.0001919343 max resid 0.0004278425
## ... Similar to previous best
## Run 16 stress 9.837467e-05
## ... Procrustes: rmse 0.0001113756 max resid 0.0002920375
## ... Similar to previous best
## Run 17 stress 9.778157e-05
## ... Procrustes: rmse 0.0001666583 max resid 0.0004155226
## ... Similar to previous best
## Run 18 stress 8.942184e-05
## ... New best solution
## ... Procrustes: rmse 0.0001457429 max resid 0.000271445
## ... Similar to previous best
## Run 19 stress 9.537981e-05
## ... Procrustes: rmse 0.000174662 max resid 0.0003249604
## ... Similar to previous best
## Run 20 stress 9.798832e-05
## ... Procrustes: rmse 0.000182358 max resid 0.000335553
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.001242547
## Run 1 stress 9.71853e-05
## ... New best solution
## ... Procrustes: rmse 0.007122439 max resid 0.01353808
## Run 2 stress 0.002928554
## Run 3 stress 0.001831015
## Run 4 stress 0.00123311
## Run 5 stress 0.001590412
## Run 6 stress 9.871512e-05
## ... Procrustes: rmse 0.0003809489 max resid 0.0007514137
## ... Similar to previous best
## Run 7 stress 0.002946181
## Run 8 stress 0.001691645
## Run 9 stress 0.001060364
## Run 10 stress 0.001268543
## Run 11 stress 0.0006010893
## Run 12 stress 9.232917e-05
## ... New best solution
## ... Procrustes: rmse 0.0001333834 max resid 0.0002962656
## ... Similar to previous best
## Run 13 stress 0.2625519
## Run 14 stress 0.002211623
## Run 15 stress 0.0009552186
## Run 16 stress 9.388226e-05
## ... Procrustes: rmse 0.0001730497 max resid 0.0004371434
## ... Similar to previous best
## Run 17 stress 0.001895442
## Run 18 stress 0.001596025
## Run 19 stress 0.0007113803
## Run 20 stress 0.0004222481
## ... Procrustes: rmse 0.002380513 max resid 0.004630478
## ... Similar to previous best
## *** Solution reached
## INFO [2020-08-20 16:22:31] Done.
## INFO [2020-08-20 16:22:31] Saving ...
## INFO [2020-08-20 16:22:33] Supplement Beta plots ...
## INFO [2020-08-20 16:22:33] Done.
## INFO [2020-08-20 16:22:33] Global1...
## [1] ""
## INFO [2020-08-20 16:22:33] No glom ...
## INFO [2020-08-20 16:22:33] Bray ...
## souche
## temps mutant sauvage
## t0 6 6
## t50 6 6
## INFO [2020-08-20 16:22:34] Done
## INFO [2020-08-20 16:22:34] Unifrac ...
## INFO [2020-08-20 16:22:34] Done
## INFO [2020-08-20 16:22:34] wunifrac ...
## INFO [2020-08-20 16:22:34] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.5075 0.50751 3.1218 0.06845 0.010989 *
## temps 1 2.1846 2.18458 13.4380 0.29463 0.000999 ***
## souche 1 1.4711 1.47112 9.0493 0.19841 0.000999 ***
## Residuals 20 3.2514 0.16257 0.43851
## Total 23 7.4146 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted
## 1 t0-sauvage vs t0-mutant 1 0.952842 4.640344 0.3169559 0.010 0.0100
## 2 t0-sauvage vs t50-sauvage 1 2.020676 28.967360 0.7433750 0.004 0.0048
## 3 t0-sauvage vs t50-mutant 1 2.197269 26.004113 0.7222540 0.003 0.0045
## 4 t0-mutant vs t50-sauvage 1 1.680832 11.591365 0.5368519 0.002 0.0045
## 5 t0-mutant vs t50-mutant 1 1.569713 9.826226 0.4956176 0.002 0.0045
## 6 t50-sauvage vs t50-mutant 1 1.817719 75.219295 0.8826557 0.003 0.0045
## sig
## 1 *
## 2 *
## 3 *
## 4 *
## 5 *
## 6 *
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.14371 0.14371 2.4348 0.04803 0.049950 *
## temps 1 1.03178 1.03178 17.4817 0.34487 0.000999 ***
## souche 1 0.63586 0.63586 10.7735 0.21254 0.000999 ***
## Residuals 20 1.18041 0.05902 0.39456
## Total 23 2.99175 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted
## 1 t0-sauvage vs t0-mutant 1 0.2393236 3.315767 0.2490106 0.005 0.005
## 2 t0-sauvage vs t50-sauvage 1 0.5252573 11.751272 0.5402568 0.005 0.005
## 3 t0-sauvage vs t50-mutant 1 1.0634198 21.786254 0.6853986 0.004 0.005
## 4 t0-mutant vs t50-sauvage 1 0.6374245 14.266849 0.5879152 0.003 0.005
## 5 t0-mutant vs t50-mutant 1 0.8716882 17.865276 0.6411304 0.001 0.005
## 6 t50-sauvage vs t50-mutant 1 0.7765885 36.437712 0.7846578 0.002 0.005
## sig
## 1 *
## 2 *
## 3 *
## 4 *
## 5 *
## 6 *
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.37184 0.37184 6.7259 0.15593 0.000999 ***
## temps 1 0.54053 0.54053 9.7773 0.22667 0.000999 ***
## souche 1 0.36665 0.36665 6.6321 0.15375 0.000999 ***
## Residuals 20 1.10568 0.05528 0.46366
## Total 23 2.38470 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted
## 1 t0-sauvage vs t0-mutant 1 0.2990497 3.044468 0.2333915 0.045 0.0450
## 2 t0-sauvage vs t50-sauvage 1 0.5797822 10.671909 0.5162517 0.001 0.0030
## 3 t0-sauvage vs t50-mutant 1 0.7469215 12.936822 0.5640198 0.002 0.0030
## 4 t0-mutant vs t50-sauvage 1 0.3028129 6.616965 0.3982054 0.002 0.0030
## 5 t0-mutant vs t50-mutant 1 0.3699053 7.522786 0.4293145 0.001 0.0030
## 6 t50-sauvage vs t50-mutant 1 0.4009432 76.052249 0.8837915 0.004 0.0048
## sig
## 1 .
## 2 *
## 3 *
## 4 *
## 5 *
## 6 *
## INFO [2020-08-20 16:22:34] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764
## Run 1 stress 0.1337253
## Run 2 stress 0.1282052
## Run 3 stress 0.1282057
## Run 4 stress 0.1004882
## ... Procrustes: rmse 0.00581795 max resid 0.02233252
## Run 5 stress 0.1004882
## ... Procrustes: rmse 0.005815793 max resid 0.02232645
## Run 6 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 9.5663e-06 max resid 3.522038e-05
## ... Similar to previous best
## Run 7 stress 0.1278059
## Run 8 stress 0.1004884
## ... Procrustes: rmse 0.005827579 max resid 0.02235
## Run 9 stress 0.1004882
## ... Procrustes: rmse 0.005829094 max resid 0.02236715
## Run 10 stress 0.1004882
## ... Procrustes: rmse 0.005820554 max resid 0.02234192
## Run 11 stress 0.1004764
## ... Procrustes: rmse 4.844533e-05 max resid 0.000148011
## ... Similar to previous best
## Run 12 stress 0.1004882
## ... Procrustes: rmse 0.005806417 max resid 0.02231016
## Run 13 stress 0.1004765
## ... Procrustes: rmse 8.616688e-05 max resid 0.0002323718
## ... Similar to previous best
## Run 14 stress 0.1004764
## ... Procrustes: rmse 2.073449e-05 max resid 3.53979e-05
## ... Similar to previous best
## Run 15 stress 0.1322893
## Run 16 stress 0.1004882
## ... Procrustes: rmse 0.005826637 max resid 0.02236303
## Run 17 stress 0.1004764
## ... Procrustes: rmse 2.217978e-05 max resid 3.975911e-05
## ... Similar to previous best
## Run 18 stress 0.1004882
## ... Procrustes: rmse 0.005826171 max resid 0.02235134
## Run 19 stress 0.1004765
## ... Procrustes: rmse 7.112848e-05 max resid 0.0002076316
## ... Similar to previous best
## Run 20 stress 0.1004764
## ... Procrustes: rmse 5.020109e-05 max resid 0.0001032207
## ... Similar to previous best
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882
## Run 1 stress 0.1282054
## Run 2 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 0.005815739 max resid 0.02229399
## Run 3 stress 0.1322874
## Run 4 stress 0.1004883
## ... Procrustes: rmse 0.005799368 max resid 0.02233062
## Run 5 stress 0.1278333
## Run 6 stress 0.1282048
## Run 7 stress 0.1278136
## Run 8 stress 0.1278166
## Run 9 stress 0.1004764
## ... Procrustes: rmse 1.375747e-05 max resid 2.676552e-05
## ... Similar to previous best
## Run 10 stress 0.1004764
## ... Procrustes: rmse 1.206258e-05 max resid 2.852253e-05
## ... Similar to previous best
## Run 11 stress 0.1004764
## ... Procrustes: rmse 1.697751e-05 max resid 5.177575e-05
## ... Similar to previous best
## Run 12 stress 0.1004882
## ... Procrustes: rmse 0.00581142 max resid 0.02230079
## Run 13 stress 0.1282048
## Run 14 stress 0.1004883
## ... Procrustes: rmse 0.005833772 max resid 0.02232659
## Run 15 stress 0.1004882
## ... Procrustes: rmse 0.005824392 max resid 0.02233711
## Run 16 stress 0.1004764
## ... Procrustes: rmse 1.842712e-05 max resid 6.09228e-05
## ... Similar to previous best
## Run 17 stress 0.1004882
## ... Procrustes: rmse 0.005796345 max resid 0.02220223
## Run 18 stress 0.1004765
## ... Procrustes: rmse 6.12507e-05 max resid 0.0001541102
## ... Similar to previous best
## Run 19 stress 0.1004882
## ... Procrustes: rmse 0.005814297 max resid 0.02230969
## Run 20 stress 0.1278114
## *** Solution reached
## Run 0 stress 0.1230986
## Run 1 stress 0.173498
## Run 2 stress 0.1232368
## ... Procrustes: rmse 0.008672617 max resid 0.03032075
## Run 3 stress 0.1232368
## ... Procrustes: rmse 0.008663607 max resid 0.0302901
## Run 4 stress 0.1232368
## ... Procrustes: rmse 0.008658079 max resid 0.03026862
## Run 5 stress 0.1232368
## ... Procrustes: rmse 0.008666702 max resid 0.03029997
## Run 6 stress 0.1230986
## ... Procrustes: rmse 7.354334e-05 max resid 0.0002143172
## ... Similar to previous best
## Run 7 stress 0.123274
## ... Procrustes: rmse 0.005402749 max resid 0.01785782
## Run 8 stress 0.1232368
## ... Procrustes: rmse 0.008658476 max resid 0.03027094
## Run 9 stress 0.1230985
## ... New best solution
## ... Procrustes: rmse 3.536316e-05 max resid 0.0001358091
## ... Similar to previous best
## Run 10 stress 0.1232368
## ... Procrustes: rmse 0.008666399 max resid 0.03030248
## Run 11 stress 0.1232368
## ... Procrustes: rmse 0.008661719 max resid 0.03028381
## Run 12 stress 0.1671132
## Run 13 stress 0.1671104
## Run 14 stress 0.1232368
## ... Procrustes: rmse 0.008673705 max resid 0.03032571
## Run 15 stress 0.1232368
## ... Procrustes: rmse 0.008676942 max resid 0.03033842
## Run 16 stress 0.1232368
## ... Procrustes: rmse 0.008670573 max resid 0.03031969
## Run 17 stress 0.1232368
## ... Procrustes: rmse 0.008667618 max resid 0.03030295
## Run 18 stress 0.1669265
## Run 19 stress 0.1734981
## Run 20 stress 0.1232368
## ... Procrustes: rmse 0.008673297 max resid 0.03032551
## *** Solution reached
## Run 0 stress 0.08834803
## Run 1 stress 0.08834753
## ... New best solution
## ... Procrustes: rmse 0.0007319815 max resid 0.00298183
## ... Similar to previous best
## Run 2 stress 0.09399784
## Run 3 stress 0.0935419
## Run 4 stress 0.08834743
## ... New best solution
## ... Procrustes: rmse 0.0003040748 max resid 0.00120801
## ... Similar to previous best
## Run 5 stress 0.08834736
## ... New best solution
## ... Procrustes: rmse 0.0001113763 max resid 0.0004374654
## ... Similar to previous best
## Run 6 stress 0.1353625
## Run 7 stress 0.08834747
## ... Procrustes: rmse 0.0001356072 max resid 0.0005634684
## ... Similar to previous best
## Run 8 stress 0.08834776
## ... Procrustes: rmse 0.0003060515 max resid 0.00100127
## ... Similar to previous best
## Run 9 stress 0.08834815
## ... Procrustes: rmse 0.0004330635 max resid 0.00184907
## ... Similar to previous best
## Run 10 stress 0.0883478
## ... Procrustes: rmse 0.0003098356 max resid 0.001320679
## ... Similar to previous best
## Run 11 stress 0.08834728
## ... New best solution
## ... Procrustes: rmse 8.683843e-05 max resid 0.0003166459
## ... Similar to previous best
## Run 12 stress 0.08834728
## ... New best solution
## ... Procrustes: rmse 8.439185e-05 max resid 0.0003479977
## ... Similar to previous best
## Run 13 stress 0.1379554
## Run 14 stress 0.1379528
## Run 15 stress 0.08834785
## ... Procrustes: rmse 0.0004566997 max resid 0.001869886
## ... Similar to previous best
## Run 16 stress 0.09399776
## Run 17 stress 0.1379538
## Run 18 stress 0.08834927
## ... Procrustes: rmse 0.0006699547 max resid 0.002737435
## ... Similar to previous best
## Run 19 stress 0.09399662
## Run 20 stress 0.08834845
## ... Procrustes: rmse 0.0004899353 max resid 0.001999688
## ... Similar to previous best
## *** Solution reached
## INFO [2020-08-20 16:22:35] Done.
## INFO [2020-08-20 16:22:35] Saving ...
## INFO [2020-08-20 16:22:38] Supplement Beta plots ...
## INFO [2020-08-20 16:22:38] Done.
## INFO [2020-08-20 16:22:38] Global2...
## [1] ""
## INFO [2020-08-20 16:22:38] No glom ...
## INFO [2020-08-20 16:22:38] Bray ...
##
## t0 t50
## 12 12
## INFO [2020-08-20 16:22:38] Done
## INFO [2020-08-20 16:22:38] Unifrac ...
## INFO [2020-08-20 16:22:38] Done
## INFO [2020-08-20 16:22:38] wunifrac ...
## INFO [2020-08-20 16:22:38] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.5075 0.50751 2.2568 0.06845 0.060939 .
## temps 1 2.1846 2.18458 9.7144 0.29463 0.000999 ***
## Residuals 21 4.7225 0.22488 0.63692
## Total 23 7.4146 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 t0 vs t50 1 2.348965 10.20159 0.316804 0.001 0.001 **
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.13714 0.13714 1.5795 0.04693 0.155844
## temps 1 0.96203 0.96203 11.0799 0.32918 0.000999 ***
## Residuals 21 1.82337 0.08683 0.62390
## Total 23 2.92255 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 t0 vs t50 1 0.9675817 10.88858 0.3310748 0.001 0.001 **
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.11324 0.113242 4.7188 0.14109 0.002997 **
## temps 1 0.18544 0.185436 7.7272 0.23103 0.000999 ***
## Residuals 21 0.50396 0.023998 0.62788
## Total 23 0.80263 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 t0 vs t50 1 0.2168864 8.146016 0.2702187 0.001 0.001 **
## INFO [2020-08-20 16:22:38] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764
## Run 1 stress 0.1004882
## ... Procrustes: rmse 0.0058152 max resid 0.02231829
## Run 2 stress 0.1282048
## Run 3 stress 0.1004882
## ... Procrustes: rmse 0.005806771 max resid 0.02229052
## Run 4 stress 0.1004884
## ... Procrustes: rmse 0.005777356 max resid 0.02224024
## Run 5 stress 0.1278246
## Run 6 stress 0.1004882
## ... Procrustes: rmse 0.005818638 max resid 0.02234708
## Run 7 stress 0.1332837
## Run 8 stress 0.1004765
## ... Procrustes: rmse 0.0001182885 max resid 0.0003080071
## ... Similar to previous best
## Run 9 stress 0.1278094
## Run 10 stress 0.1004765
## ... Procrustes: rmse 0.0001079871 max resid 0.0002819903
## ... Similar to previous best
## Run 11 stress 0.1332837
## Run 12 stress 0.1316113
## Run 13 stress 0.1337298
## Run 14 stress 0.1004882
## ... Procrustes: rmse 0.005798541 max resid 0.02228347
## Run 15 stress 0.1004764
## ... Procrustes: rmse 3.126013e-05 max resid 7.168403e-05
## ... Similar to previous best
## Run 16 stress 0.1004766
## ... Procrustes: rmse 5.155549e-05 max resid 0.0001446153
## ... Similar to previous best
## Run 17 stress 0.1004882
## ... Procrustes: rmse 0.005800221 max resid 0.02228243
## Run 18 stress 0.1004764
## ... Procrustes: rmse 8.623673e-06 max resid 1.750607e-05
## ... Similar to previous best
## Run 19 stress 0.1004882
## ... Procrustes: rmse 0.005793156 max resid 0.02226272
## Run 20 stress 0.3840802
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882
## Run 1 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 0.005814918 max resid 0.02227738
## Run 2 stress 0.1316123
## Run 3 stress 0.1278166
## Run 4 stress 0.1278225
## Run 5 stress 0.1332155
## Run 6 stress 0.1004766
## ... Procrustes: rmse 9.205958e-05 max resid 0.0002457274
## ... Similar to previous best
## Run 7 stress 0.1322907
## Run 8 stress 0.1274643
## Run 9 stress 0.1278115
## Run 10 stress 0.1004882
## ... Procrustes: rmse 0.005806634 max resid 0.02227474
## Run 11 stress 0.1004882
## ... Procrustes: rmse 0.005809967 max resid 0.02229281
## Run 12 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 8.370976e-05 max resid 0.0002461561
## ... Similar to previous best
## Run 13 stress 0.1004882
## ... Procrustes: rmse 0.005821153 max resid 0.0223582
## Run 14 stress 0.1282054
## Run 15 stress 0.1332827
## Run 16 stress 0.1338151
## Run 17 stress 0.1004882
## ... Procrustes: rmse 0.005818853 max resid 0.0223352
## Run 18 stress 0.1004766
## ... Procrustes: rmse 0.0001268051 max resid 0.0003952689
## ... Similar to previous best
## Run 19 stress 0.1275919
## Run 20 stress 0.1004765
## ... Procrustes: rmse 6.635743e-05 max resid 0.0001774361
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.1211578
## Run 1 stress 0.1211578
## ... Procrustes: rmse 2.714291e-06 max resid 5.639292e-06
## ... Similar to previous best
## Run 2 stress 0.1660951
## Run 3 stress 0.1220808
## Run 4 stress 0.1211578
## ... Procrustes: rmse 3.110957e-06 max resid 1.043545e-05
## ... Similar to previous best
## Run 5 stress 0.1220804
## Run 6 stress 0.1208049
## ... New best solution
## ... Procrustes: rmse 0.01452926 max resid 0.05165115
## Run 7 stress 0.1211578
## ... Procrustes: rmse 0.01452871 max resid 0.05175567
## Run 8 stress 0.1218747
## Run 9 stress 0.1211578
## ... Procrustes: rmse 0.01452911 max resid 0.05175412
## Run 10 stress 0.120805
## ... Procrustes: rmse 3.446316e-05 max resid 9.502088e-05
## ... Similar to previous best
## Run 11 stress 0.166108
## Run 12 stress 0.1220804
## Run 13 stress 0.1220804
## Run 14 stress 0.1211578
## ... Procrustes: rmse 0.01453088 max resid 0.05175291
## Run 15 stress 0.1224642
## Run 16 stress 0.1227048
## Run 17 stress 0.1223135
## Run 18 stress 0.1218747
## Run 19 stress 0.1211578
## ... Procrustes: rmse 0.01452661 max resid 0.05175859
## Run 20 stress 0.1729678
## *** Solution reached
## Run 0 stress 0.07441356
## Run 1 stress 0.08050991
## Run 2 stress 0.07522281
## Run 3 stress 0.07477326
## ... Procrustes: rmse 0.00814833 max resid 0.0325014
## Run 4 stress 0.09301933
## Run 5 stress 0.07441314
## ... New best solution
## ... Procrustes: rmse 0.0001825381 max resid 0.0004760459
## ... Similar to previous best
## Run 6 stress 0.09333941
## Run 7 stress 0.08075318
## Run 8 stress 0.0747723
## ... Procrustes: rmse 0.008351518 max resid 0.03396039
## Run 9 stress 0.07522104
## Run 10 stress 0.07441185
## ... New best solution
## ... Procrustes: rmse 0.0003876495 max resid 0.001061552
## ... Similar to previous best
## Run 11 stress 0.09302056
## Run 12 stress 0.07441187
## ... Procrustes: rmse 0.0002135307 max resid 0.0006217941
## ... Similar to previous best
## Run 13 stress 0.07441387
## ... Procrustes: rmse 0.001058677 max resid 0.002298426
## ... Similar to previous best
## Run 14 stress 0.08075309
## Run 15 stress 0.07997456
## Run 16 stress 0.0744119
## ... Procrustes: rmse 0.0005426132 max resid 0.00129187
## ... Similar to previous best
## Run 17 stress 0.07477255
## ... Procrustes: rmse 0.008507167 max resid 0.03478628
## Run 18 stress 0.08046399
## Run 19 stress 0.08050348
## Run 20 stress 0.08043635
## *** Solution reached
## INFO [2020-08-20 16:22:39] Done.
## INFO [2020-08-20 16:22:39] Saving ...
## INFO [2020-08-20 16:22:41] Supplement Beta plots ...
## INFO [2020-08-20 16:22:41] Done.
## INFO [2020-08-20 16:22:41] Global3...
## [1] ""
## INFO [2020-08-20 16:22:41] No glom ...
## INFO [2020-08-20 16:22:41] Bray ...
##
## mutant sauvage
## 12 12
## INFO [2020-08-20 16:22:41] Done
## INFO [2020-08-20 16:22:41] Unifrac ...
## INFO [2020-08-20 16:22:42] Done
## INFO [2020-08-20 16:22:42] wunifrac ...
## INFO [2020-08-20 16:22:42] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.5075 0.50751 1.9574 0.06845 0.078921 .
## souche 1 1.4622 1.46217 5.6393 0.19720 0.000999 ***
## Residuals 21 5.4449 0.25928 0.73435
## Total 23 7.4146 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 1.529137 5.715979 0.2062341 0.001 0.001 **
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.14509 0.14509 1.3503 0.04777 0.250749
## souche 1 0.63543 0.63543 5.9138 0.20923 0.003996 **
## Residuals 21 2.25640 0.10745 0.74299
## Total 23 3.03692 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.6717987 6.248969 0.2212105 0.001 0.001 **
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.19505 0.19505 2.7736 0.09195 0.024975 *
## souche 1 0.44934 0.44934 6.3897 0.21184 0.000999 ***
## Residuals 21 1.47676 0.07032 0.69621
## Total 23 2.12115 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.4476574 5.884986 0.211045 0.001 0.001 **
## INFO [2020-08-20 16:22:42] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764
## Run 1 stress 0.1004764
## ... Procrustes: rmse 2.847012e-05 max resid 7.721061e-05
## ... Similar to previous best
## Run 2 stress 0.1332144
## Run 3 stress 0.1004765
## ... Procrustes: rmse 0.0001032707 max resid 0.000269979
## ... Similar to previous best
## Run 4 stress 0.1278064
## Run 5 stress 0.1004882
## ... Procrustes: rmse 0.005821273 max resid 0.02233913
## Run 6 stress 0.1004882
## ... Procrustes: rmse 0.005816364 max resid 0.02232368
## Run 7 stress 0.1004765
## ... Procrustes: rmse 6.90138e-05 max resid 0.0001834775
## ... Similar to previous best
## Run 8 stress 0.1004882
## ... Procrustes: rmse 0.005814011 max resid 0.02231602
## Run 9 stress 0.1322888
## Run 10 stress 0.1282046
## Run 11 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 7.282923e-06 max resid 1.664746e-05
## ... Similar to previous best
## Run 12 stress 0.1004883
## ... Procrustes: rmse 0.005784473 max resid 0.02224248
## Run 13 stress 0.1004764
## ... Procrustes: rmse 1.456917e-05 max resid 3.850938e-05
## ... Similar to previous best
## Run 14 stress 0.1004882
## ... Procrustes: rmse 0.005846599 max resid 0.02244329
## Run 15 stress 0.1372463
## Run 16 stress 0.1278191
## Run 17 stress 0.1004765
## ... Procrustes: rmse 6.182907e-05 max resid 0.0001456336
## ... Similar to previous best
## Run 18 stress 0.1004769
## ... Procrustes: rmse 7.942311e-05 max resid 0.0002831982
## ... Similar to previous best
## Run 19 stress 0.1275855
## Run 20 stress 0.128205
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882
## Run 1 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 0.005815834 max resid 0.02229868
## Run 2 stress 0.1004882
## ... Procrustes: rmse 0.005807534 max resid 0.0222804
## Run 3 stress 0.1278065
## Run 4 stress 0.1004883
## ... Procrustes: rmse 0.005823992 max resid 0.02238489
## Run 5 stress 0.1004764
## ... Procrustes: rmse 1.060961e-05 max resid 2.872919e-05
## ... Similar to previous best
## Run 6 stress 0.1004765
## ... Procrustes: rmse 7.285362e-05 max resid 0.0001739838
## ... Similar to previous best
## Run 7 stress 0.1282046
## Run 8 stress 0.1316111
## Run 9 stress 0.1332147
## Run 10 stress 0.1275819
## Run 11 stress 0.1004764
## ... Procrustes: rmse 6.523432e-06 max resid 2.205578e-05
## ... Similar to previous best
## Run 12 stress 0.1004766
## ... Procrustes: rmse 0.0001054406 max resid 0.0002899242
## ... Similar to previous best
## Run 13 stress 0.1004764
## ... Procrustes: rmse 4.547765e-06 max resid 8.111205e-06
## ... Similar to previous best
## Run 14 stress 0.1004884
## ... Procrustes: rmse 0.005826732 max resid 0.0223516
## Run 15 stress 0.1004766
## ... Procrustes: rmse 5.296991e-05 max resid 0.0001744325
## ... Similar to previous best
## Run 16 stress 0.1004882
## ... Procrustes: rmse 0.005813939 max resid 0.02231234
## Run 17 stress 0.1004764
## ... Procrustes: rmse 6.363861e-05 max resid 0.000133539
## ... Similar to previous best
## Run 18 stress 0.1278136
## Run 19 stress 0.1278183
## Run 20 stress 0.1004882
## ... Procrustes: rmse 0.005808462 max resid 0.02229637
## *** Solution reached
## Run 0 stress 0.1228815
## Run 1 stress 0.1225281
## ... New best solution
## ... Procrustes: rmse 0.01736792 max resid 0.05438397
## Run 2 stress 0.1228815
## ... Procrustes: rmse 0.01736807 max resid 0.05454788
## Run 3 stress 0.1228749
## ... Procrustes: rmse 0.009103093 max resid 0.03405979
## Run 4 stress 0.1237
## Run 5 stress 0.1223269
## ... New best solution
## ... Procrustes: rmse 0.008473442 max resid 0.02951088
## Run 6 stress 0.1237001
## Run 7 stress 0.1228816
## Run 8 stress 0.1223269
## ... Procrustes: rmse 1.202391e-05 max resid 3.908702e-05
## ... Similar to previous best
## Run 9 stress 0.1237
## Run 10 stress 0.1228749
## Run 11 stress 0.1223269
## ... Procrustes: rmse 1.380151e-05 max resid 4.682433e-05
## ... Similar to previous best
## Run 12 stress 0.123224
## Run 13 stress 0.3783049
## Run 14 stress 0.1228749
## Run 15 stress 0.1228815
## Run 16 stress 0.1237
## Run 17 stress 0.1228749
## Run 18 stress 0.1225281
## ... Procrustes: rmse 0.008478317 max resid 0.02949272
## Run 19 stress 0.1225281
## ... Procrustes: rmse 0.008474735 max resid 0.02947121
## Run 20 stress 0.1228815
## *** Solution reached
## Run 0 stress 0.09132816
## Run 1 stress 0.1324532
## Run 2 stress 0.2381793
## Run 3 stress 0.09132823
## ... Procrustes: rmse 9.584463e-05 max resid 0.0003505044
## ... Similar to previous best
## Run 4 stress 0.0959093
## Run 5 stress 0.1312552
## Run 6 stress 0.0913284
## ... Procrustes: rmse 4.735323e-05 max resid 0.0001520468
## ... Similar to previous best
## Run 7 stress 0.09132813
## ... New best solution
## ... Procrustes: rmse 0.0001364362 max resid 0.0004802557
## ... Similar to previous best
## Run 8 stress 0.1603723
## Run 9 stress 0.09132809
## ... New best solution
## ... Procrustes: rmse 8.667887e-05 max resid 0.000337298
## ... Similar to previous best
## Run 10 stress 0.1324533
## Run 11 stress 0.131847
## Run 12 stress 0.1678831
## Run 13 stress 0.1579075
## Run 14 stress 0.09132815
## ... Procrustes: rmse 8.327058e-05 max resid 0.0003190981
## ... Similar to previous best
## Run 15 stress 0.1578989
## Run 16 stress 0.09590905
## Run 17 stress 0.2320746
## Run 18 stress 0.09633224
## Run 19 stress 0.09633088
## Run 20 stress 0.09132817
## ... Procrustes: rmse 0.0001071494 max resid 0.0004339076
## ... Similar to previous best
## *** Solution reached
## INFO [2020-08-20 16:22:43] Done.
## INFO [2020-08-20 16:22:43] Saving ...
## INFO [2020-08-20 16:22:45] Supplement Beta plots ...
## INFO [2020-08-20 16:22:45] Done.
## INFO [2020-08-20 16:22:45] Finish